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Normalize units & ranges

normalize_series
Read-onlyIdempotent

Converts a health signal's readings to a common unit and adds a reference position, enabling direct comparison of measurements taken with different units or reference ranges.

Instructions

Reconcile mixed units and reference ranges within one signal.

Pulls a signal's readings, converts every value (and its reference range, for labs/biomarkers) to a single common unit, and adds a unitless 'reference position' so readings taken with different units or reference ranges become directly comparable.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesthe signal name, e.g. 'glucose' or 'a1c_percent'.
userNowhich person; defaults to the primary user.
limitNomax readings to return.
sinceNo
untilNo
sourceNo'lab' | 'biomarker' | 'metric' | 'wearable' | 'substance'.lab
analyteNoanalyte hint (e.g. 'glucose', 'cholesterol', 'creatinine') that unlocks mass<->molar conversions like mg/dL<->mmol/L; defaults to the signal name.
to_unitNotarget unit for all values; omit to use the most common unit already present in the series.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnlyHint and idempotentHint annotations, the description adds that the tool converts values and reference ranges, and adds a unitless reference position. This provides useful behavioral context without contradicting annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, front-loaded with the core purpose, and structured in two sentences. Every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 8 parameters and an output schema, the description adequately covers the primary transformation. It lacks details on filtering parameters (since/until) but these are standard. The output schema provides return format information, so the description is sufficiently complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 75% schema description coverage, most parameters are already described. The tool description does not elaborate on individual parameters, but it contextualizes the overall normalization process, which indirectly aids understanding. Baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool 'reconciles mixed units and reference ranges within one signal,' using a specific verb and resource. It then explains the process in detail, distinguishing its normalization purpose from other analysis tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not explicitly state when to use this tool versus alternatives like align_series or analyze_trend. Usage is implied by the normalization function, but no direct guidance is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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